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Forecasting method of national-level forest fire risk rating

机译：国家级森林火灾风险评级的预测方法

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The risk level of forest fire not only depends on weather, topography, human activities, socio-economic conditions, but is also closely related to the types, growth, moisture content, and quantity of forest fuel on the ground. How to timely acquire information about the growth and moisture content of forest fuel and climate for the whole country is critical to national-level forest fire risk forecasting. The development and application of remote sensing (RS), geographic information system (GIS), databases, internet, and other modern information technologies has provided important technical means for macro-regional forest fire risk forecasting. In this paper, quantified forecasting of national-level forest fire risk was studied using Fuel State Index (FSI) and Background Composite Index (BCI). The FSI was estimated using Moderate Resolution Imaging Spectroradiaometer (MODIS) data. National meteorological data and other basic data on distribution of fuel types and forest fire risk rating were standardized in ArcGIS platform to calculate BCI. The FSI and the BCI were used to calculate the Forest Fire Danger Index (FFDI), which is regarded as a quantitative indicator for national forest fire risk forecasting and forest fire risk rating, shifting from qualitative description to quantitative estimation. The major forest fires occurred in recent years were taken as examples to validate the above method, and results indicated that the method can be used for quantitative forecasting of national-level forest fire risks.
机译：森林火灾风险水平不仅取决于天气，地形，人类活动，社会经济条件，还与地面上森林燃料的类型，生长，水分含量和数量密切相关。如何及时获取有关森林燃料的增长和水分含量和全国气候的信息对国家一级森林火灾风险预测至关重要。遥感（RS），地理信息系统（GIS），数据库，互联网等现代信息技术的开发和应用为宏观区域森林火灾风险预测提供了重要的技术手段。本文研究了利用燃料态指数（FSI）和背景综合指数（BCI）研究了国家一级森林火灾风险的量化预测。使用中度分辨率成像光谱分析值（MODIS）数据估计FSI。国家气象数据和关于燃料类型和森林火灾风险评级分布的其他基本数据在ArcGIS平台中标准化以计算BCI。 FSI和BCI用于计算森林火灾危险指数（FFDI），被认为是国家森林火灾风险预测和森林火灾风险评级的定量指标，从定性描述转移到定量估计。近年来的主要森林火灾是验证上述方法的例子，结果表明，该方法可用于全国林灾风险的定量预测。

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